Scientific discoveries increasingly rely on the ability to efficiently grind massive amounts of experimental data using database technologies. To bridge the gap between the needs of the Data-Intensive Research fields and the current DBMS technologies, we are developing SciQL (pronounced as ‘cycle’), an SQL-based query language for scientific applications with both tables and arrays as first class citizens. It provides a seamless symbiosis of array-, set- and sequence- interpretations. A key innovation is the extension of value-based grouping of SQL:2003 with structural grouping, i.e., fixed-sized and unbounded groups based on explicit relationships between elements positions. This leads to a generalisation of window-based query processing with wide applicability in science domains. In this demo, we show the main features of SciQL using use cases of remote sensing image processing.
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PlanetData
Database Architectures

Zhang, Y, & Kersten, M.L. (2012). Scientific Data Processing Using SciQL.